library("tidyverse")
## ── Attaching packages ───────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ──────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
surveys_complete <- read_csv("data/surveys_complete.csv")
## Parsed with column specification:
## cols(
##   record_id = col_double(),
##   month = col_double(),
##   day = col_double(),
##   year = col_double(),
##   plot_id = col_double(),
##   species_id = col_character(),
##   sex = col_character(),
##   hindfoot_length = col_double(),
##   weight = col_double(),
##   genus = col_character(),
##   species = col_character(),
##   taxa = col_character(),
##   plot_type = col_character()
## )
# Assign plot to a variable
surveys_plot <- ggplot(data = surveys_complete, 
                       mapping = aes(x = weight, y = hindfoot_length))

# Draw the plot
surveys_plot + 
    geom_point()

### Challenge

library(hexbin)
surveys_plot +
  geom_hex()

# Building your plot iteratively

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1, aes(color = species_id))

Challenge

ggplot(data = surveys_complete, 
       aes(x = species_id, y = weight)) +
    geom_point(aes(color = plot_type))

# Boxplot

ggplot(data = surveys_complete,
       mapping = aes(x = species_id, y = weight)) + 
  geom_jitter(alpha = 0.3, color = "tomato") +
  geom_boxplot(alpha = 0)

### Challenges

ggplot(data = surveys_complete,
       mapping = aes(x = species_id, y = weight)) +
  geom_violin() +
    scale_y_log10()

ggplot(data = surveys_complete,
       mapping = aes(x = species_id, y = hindfoot_length)) +
  geom_jitter(alpha = 0.3, color = "blue") +
  geom_violin()

Plotting time series data

yearly_counts <- surveys_complete %>% 
  count(year, genus)
ggplot(data = yearly_counts, 
       aes(x = year, y = n, color = genus)) + 
  geom_line()

yearly_counts %>% 
  ggplot(mapping = aes(x = year, y = n, color = genus)) +
  geom_line()

yearly_counts_graph <- surveys_complete %>%
  count(year, genus) %>% 
  ggplot(mapping = aes(x = year, y = n, color = genus)) +
  geom_line()
yearly_counts_graph

Faceting

ggplot(data = yearly_counts,
       aes(x = year, y = n)) +
  geom_line() + 
  facet_wrap(facets = vars(genus))

yearly_sex_counts <- surveys_complete %>% 
  count(year, genus, sex)
ggplot(data = yearly_sex_counts,
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() + 
  facet_wrap(facets = vars(genus))

ggplot(data = yearly_sex_counts,
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() + 
  facet_grid(rows = vars(sex), cols = vars(genus))

# One column, facet by rows
ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(rows = vars(genus))

# One row, facet by column
ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(cols = vars(genus))

ggplot themes

 ggplot(data = yearly_sex_counts, 
        mapping = aes(x = year, y = n, color = sex)) +
     geom_line() +
     facet_wrap(vars(genus)) +
     theme_bw()

Challenge

yearly_weight <- surveys_complete %>% 
  group_by(year, species_id) %>% 
  summarize(average_weight = mean(weight))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(data = yearly_weight,
       mapping = aes(x = year, y = average_weight)) +
  geom_line() + 
  facet_wrap(vars(species_id)) +
  theme_bw()

Customization

ggplot(data = yearly_sex_counts,
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() + 
  facet_wrap(vars(genus)) +
  labs(title = "Observed genera through time",
       x = "Year of observation",
       y = "Number of individuals") +
  theme_bw() +
  theme(axis.text.x = element_text(color = "grey20", size = 12, angle = 60, hjust = 0.5, vjust = 0.5),
                      axis.text.y = element_text(color = "grey20", size = 12),
                      strip.text = element_text(face = "italic"),
                      text = element_text(size = 16))

Arranging and exporting plots

library(gridExtra)
## 
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
## 
##     combine
spp_weight_boxplot <- ggplot(data = surveys_complete, 
                             aes(x = species_id, y = weight)) +
  geom_boxplot() +
  labs(x = "Species", 
       y = expression(log[10](Weight))) +
  scale_y_log10() + 
  labs()

spp_count_plot <- ggplot(data = yearly_counts, 
                         aes(x = year, y = n, color = genus)) +
  geom_line() + 
  labs(x = "Year", y = "Abundance")

grid.arrange(spp_weight_boxplot, spp_count_plot, ncol = 2, widths = c(4, 6))

Saving

yearly_sex_counts_plot <- ggplot(data = yearly_sex_counts, 
                  aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed genera through time",
        x = "Year of observation",
        y = "Number of individuals") +
    theme_bw() +
    theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90,
                                     hjust = 0.5, vjust = 0.5),
          axis.text.y = element_text(colour = "grey20", size = 12),
          text = element_text(size = 16))

ggsave("yearly_sex_counts_plot.png", yearly_sex_counts_plot, width = 15, height = 10)

## This also works for grid.arrange() plots
combo_plot <- grid.arrange(spp_weight_boxplot, spp_count_plot, ncol = 2, 
                           widths = c(4, 6))

ggsave("combo_plot_abun_weight.png", combo_plot, width = 10, dpi = 300)
## Saving 10 x 5 in image